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Measuring Severity in Autism Spectrum Disorders

City:

Ann Arbor

State/Province:

MI

State/Province Full:

Michigan

Country:

United States

Basic and clinical autism research relies on an accurate assessment of the severity of core autism features—currently measured largely in terms of language delay and cognitive functioning. Researchers use two primary tools to rate the severity of autism symptoms among groups of patients: the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS). However, these measures are affected by verbal level, chronological age, and IQ, and are not always appropriate to compare symptom severity between children. The ADOS has been effective in categorizing children who definitely have autism or not, but is not as accurate for making distinctions involving children with milder ASDs. To address the limitations of these diagnostic measures and to make the ADOS and ADI-R experimentally useful for addressing symptom severity, Dr. Lord, along with pre-doctoral fellow Katherine Gotham, will standardize scoring of the ADOS and ADI-R. This will be done using a large database of more than 1,600 assessments from the clinic at the University of Michigan . Scores will be calibrated and standardized based on age and language level. This will allow them to approximate a scale of “autism severity” based on ADOS and ADI scores that is independent of developmental and language levels across the age range. This standardization will allow for a better comparison of assessments across time, improve inter-rater reliability, and offer an option for predicting outcome in children with ASD. What this means for people with autism: This research will re-calibrate two highly-used diagnostic tools to allow accurate comparisons of the severity of autism across groups of people. It will create and ADOS “calibrated severity metric” for use by other researchers to more accurately quantify severity of symptoms and for subtyping autism into different categories so the causes may be better explained.